Make Similar Graphs to Unclutter Data from the American Consumer Survey

Cody Steele 30 September, 2015

September 17 marked the release of new information from the American Community Survey (ACS) from the U.S. Census Bureau. Here’s a bar chart of what the press releases looked like for that day:

Most press releases from the Census Bureau about the ACS were about declines in uninsured people in major metropolitan areas.

Clearly there was a theme in play, one that was great news for major metropolitan areas. The Census Bureau even released a graph showing that the percentage of people within the 25 most populous metropolitan areas in the United States all saw declines in their percentages of uninsured people.

I tend a bit towards cynicism, so I wondered...why play up just the top 25 metro areas instead of the 50 states? It wouldn’t be that much harder to see...unless, that is, your graph is cluttered. After all, the default graph in Minitab for comparing numbers of uninsured people per state looks a bit like this:

This bar chart is too cluttered to easily identify the individual bars.

Now, I remember from my school days that we have a senate with two members from each state so that all states are represented equally. But when it comes to population, some states don’t really belong together. Case in point: when it comes to numbers of people, there’s hardly ever a reason for California and Vermont to be on the same graph.

Fortunately, Minitab makes it easy to create similar graphs of different variables in a worksheet. Let’s say that you’re starting from a worksheet like this one, where the states, Puerto Rico, and Washington, D.C., have been divided into categories based on the number of uninsured people in 2013. If you're not already using Minitab Statistical Software and you'd like to follow along, you can get it free for 30 days

First, create a graph of the large states.

  1. Choose Graph > Bar Chart.
  2. In Bars represent, select Values from a table.
  3. Under Two-way table, select Cluster. Click OK.
  4. In Graph variables, enter ‘2013_Large’ ‘2014_Large’.
  5. In Row Labels, enter ‘Region Large’.
  6. In Table Arrangement, select Rows are outermost categories and columns are innermost.
  7. Click Chart Options.
  8. In Order Main X Groups By, select Decreasing Y. Click OK in both dialog boxes.

An ordered bar chart of the states with the highest number of uninsured people in 2013.

Next, let's edit the graph.

  1. Double-click the bars.
  2. Select the Groups tab.
  3. Check Assign attributes by graph variables. Click OK.
  4. Double-click the label in the legend.
  5. Replace the variable name with only the year.
  6. Double-click the labels for the years.
  7. Select the Show tab.
  8. In Show Labels by Scale Level, uncheck the tick labels for the graph variables and the axis label for the region variable. Click OK.

An edited bar chart of the states with the most uninsured people in 2013.

If you had to repeat these steps to create graphs for the medium, small, and smallest states, it would be laborious. Fortunately, Minitab makes it quick to recreate a graph with the same edits.

  1. With the edited bar chart selected, choose Editor > Make Similar Graph.
  2. Replace the large state variables with the medium state versions of the variables. Click OK.

Medium states in terms of the number of uninsured people in 2013.

All of the edits you made to the first graph are already on the second graph! You can repeat the process just as quickly for the last two categories. 

Small states in terms of the number of uninsured people in 2013

Smallest states, Washington D.C. and Puerto Rick

With the uncluttered graphs, you can easily see that Louisiana and Nebraska are the only states that saw increases in the number of uninsured people from 2013 to 2014.

On a state-by-state level, the news looks good for the change in the number of uninsured people between 2013 and 2014. And if you need to divide up a cluttered bar chart, the news is pretty good that you have the capability to make a similar graph in Minitab. Repeating all your edits, without having to make any of them, is another way that you can get the answers you need from your analysis.


To prepare the data set I showed, I used Minitab's Unstack feature. To see that and a few other tips with worksheets, see what Eston Martz demonstrated in the helpful piece What to Do When Your Data's a Mess, part 2. Or, if you're using Minitab Express, follow along with the online example of unstacking your data.